A Framework for Long Distance Face Recognition Using Dense - and Sparse-Stereo Reconstruction

  • Authors:

  • Ham Rara;Shireen Elhabian;Asem Ali;Travis Gault;Mike Miller;Thomas Starr;Aly Farag

  • Affiliations:

  • CVIP Laboratory, University of Louisville, USA;CVIP Laboratory, University of Louisville, USA;CVIP Laboratory, University of Louisville, USA;CVIP Laboratory, University of Louisville, USA;CVIP Laboratory, University of Louisville, USA;CVIP Laboratory, University of Louisville, USA;CVIP Laboratory, University of Louisville, USA

  • Venue:
  • ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
  • Year:
  • 2009

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Abstract

This paper introduces a framework for long-distance face recognition using both dense- and sparse-stereo reconstruction. Two methods to determine correspondences of the stereo pair are used in this paper: (a) dense global stereo-matching using maximum-a-posteriori Markov Random Fields (MAP-MRF) algorithms and (b) Active Appearance Model (AAM) fitting of both images of the stereo pair and using the fitted AAM mesh as the sparse correspondences. Experiments are performed regarding the use of different features extracted from these vertices for face recognition. A comparison between the two approaches (a) and (b) are carried out in this paper. The cumulative rank curves (CMC), which are generated using the proposed framework, confirms the feasibility of the proposed work for long distance recognition of human faces.